Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction

Song, Lingyun; Sun, Xiaofan; Gan, Xinbiao; Pan, Yudai; Han, Xiaolin; Ma, Jie; Liu, Jun; Shang, Xuequn

doi:10.24963/ijcai.2025/689

Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction

Lingyun Song, Xiaofan Sun, Xinbiao Gan, Yudai Pan, Xiaolin Han, Jie Ma, Jun Liu, Xuequn Shang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 6191-6199.

https://doi.org/10.24963/ijcai.2025/689

Keywords

Machine Learning: ML: Sequence and graph learning
Data Mining: DM: Mining heterogenous data
Data Mining: DM: Mining graphs
Machine Learning: ML: Representation learning

Abstract

Considerable attention has been paid to predicting student performance on exercises. The performance of prior studies is determined by the quality of the trait features of students and exercises. Nevertheless, most of the prior study primarily examines simple pairwise interactions in learning trait features, like those between students and exercises or exercises and concepts, while disregarding the complex higher-order interactions that typically exist among these components, which in turn hinders the prediction results. In this paper, we using an innovative Multi-Channel Graph Contrastive Learning (MCGCL) framework that integrates various high-order interactions for predicting student performance. MCGCL characterizes graph structures reflecting various high-order relationships among students, exercises, and concepts through multiple channels, thereby enhancing the trait features of both students and exercises. Moreover, graph contrastive learning is employed to enhance the representation of trait features acquired from high-order graph structures in diverse views. Extensive experiments on real-world datasets show that MCGCL achieves state-of-the-art results on the task of predicting student performance. The code is available at https://github.com/sunlitsong/MCGCL.

Lingyun Song, Xiaofan Sun, Xinbiao Gan, Yudai Pan, Xiaolin Han, Jie Ma, Jun Liu, and Xuequn Shang. 2025. Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 6191–6199. https://doi.org/10.24963/ijcai.2025/689

@inproceedings{ijcai2025p689,
  title     = {Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction},
  author    = {Song, Lingyun and Sun, Xiaofan and Gan, Xinbiao and Pan, Yudai and Han, Xiaolin and Ma, Jie and Liu, Jun and Shang, Xuequn},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {6191--6199},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/689},
  url       = {https://doi.org/10.24963/ijcai.2025/689},
}

TY  - CPAPER
TI  - Metapath and Hypergraph Structure-based Multi-Channel Graph Contrastive Learning for Student Performance Prediction
AU  - Song, Lingyun
AU  - Sun, Xiaofan
AU  - Gan, Xinbiao
AU  - Pan, Yudai
AU  - Han, Xiaolin
AU  - Ma, Jie
AU  - Liu, Jun
AU  - Shang, Xuequn
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 6191
EP  - 6199
DO  - 10.24963/ijcai.2025/689
UR  - https://doi.org/10.24963/ijcai.2025/689
ER  -